NEUFEP-ME study: Functional Data Quality - summary visualizations

This document contains the code interactive visualizations for data quality checks on the 28 participants in this study.

A reminder of the study setup. Data per participant was collected in a single session. This included a single T1w image and 6 functional runs in the following order:

1. Rest Run 1
2. Motor Run 1 (right-hand finger tapping)
3. Emotion Run 1 (matching shapes and faces)
4. Rest Run 2
5. Motor Run 2 (imagined right-hand finger tapping)
6. Emotion Run 2 (recalling an emotional event)

Below are images to summarise the:

• Mean and total framewise displacement per run, for all subjects
• Framewise displacement distribution plots over all runs, for all subjects
• Framewise displacement distribution plots for sub-001 and sub-010, per run
• Framewise displacement values over time for sub-001 and sub-010, for task-emotion_run-1
• Interactive tSNR visualization of sub-001_task-emotion_run-1
• tSNR distributions across tissue types and combination methods for sub-001_task-rest_run-2
• tSNR distributions across tissue types and combination methods for sub-001_task-rest_run-2
• Interactive tSNR visualization of sub-001_task-emotion_run-1
• Interactive T-map for sub-001_task-motor_run-1, using echo 2 time series
• Interactive T-map for sub-001_task-motor_run-1, using tSNR-combined time series
• Interactive T-map for sub-001_task-motor_run-1, using T2star-combined time series
• Interactive T-map for sub-001_task-motor_run-1, using TE-combined time series

Framewise displacement (FD)

Summary data for framewise displacement (mean and sum) for all subjects

Plot FD dsitribution plots for all participants over all runs

Distribution plots for framewise displacement for sub-001 and sub-010, per runs

Framewise displacement time series plots for sub-001 and sub-010, for task-emotion_run-1

Temporal signal-to-noise ratio (tSNR)

Interactive tSNR visualization of {sub-001_task-emotion_run-1}

/Users/jheunis/miniconda3/envs/rtme-fmri/lib/python3.7/site-packages/nilearn/image/resampling.py:543: RuntimeWarning:

NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.

/Users/jheunis/miniconda3/envs/rtme-fmri/lib/python3.7/site-packages/nilearn/plotting/js_plotting_utils.py:103: UserWarning:

It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.

Ridge plots for tSNR values in different tissue masks using different echo cobinations

tSNR distributions in the whole brain across combination methods for sub-001_task-rest_run-2

tSNR distributions across tissue types for sub-001_task-rest_run-2_echo-2

Task activation

/Users/jheunis/miniconda3/envs/rtme-fmri/lib/python3.7/site-packages/nilearn/plotting/js_plotting_utils.py:103: UserWarning:

It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.

/Users/jheunis/miniconda3/envs/rtme-fmri/lib/python3.7/site-packages/nilearn/plotting/js_plotting_utils.py:103: UserWarning:

It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.

/Users/jheunis/miniconda3/envs/rtme-fmri/lib/python3.7/site-packages/nilearn/plotting/js_plotting_utils.py:103: UserWarning:

It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.

/Users/jheunis/miniconda3/envs/rtme-fmri/lib/python3.7/site-packages/nilearn/plotting/js_plotting_utils.py:103: UserWarning:

It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.

T-map for sub-001_task-motor_run-1, using echo 2 time series

T-map for sub-001_task-motor_run-1, using tSNR-combined time series

T-map for sub-001_task-motor_run-1, using T2star-combined time series

T-map for sub-001_task-motor_run-1, using TE-combined time series